The association between leptin concentration and blood coagulation: Results from the NEO study.

BACKGROUND: The adipocyte-derived hormone leptin has been associated with altered blood coagulation in in vitro studies. However, it is unclear whether this association is relevant in vivo and to what extent this association is influenced by total body fat. Therefore, we aimed to examine the association between serum leptin and blood coagulation while taking total body fat into account in a population-based cohort study. METHODS: We performed a cross-sectional analysis with baseline measurements of 5797 participants of the Netherlands Epidemiology of Obesity (NEO) study, a population-based cohort of middle-aged men and women. We examined associations between serum leptin concentration and coagulation factor concentrations and parameters of platelet activation in linear regression analyses. All analyses were adjusted for multiple covariates, including total body fat. RESULTS: In multivariable adjusted analyses a 1 µg/L higher serum leptin concentration was associated with a 0.22 IU/dL (95% CI: 0.11, 0.32) higher FVIII concentration and a 0.20 IU/dL (95% CI: 0.14, 0.27) higher FIX concentration (3.5 IU/dL FVIII and 3.2 IU/dL FIX per SD leptin). Serum leptin concentration was not associated with FXI, fibrinogen, platelet count, mean platelet volume and platelet distribution width in multivariable adjusted analyses. DISCUSSION: This study showed that serum leptin concentration was associated with higher concentrations of FVIII and FIX in an observational study, which could be clinically relevant.

High-sensitivity C-reactive protein, low-grade systemic inflammation and type 2 diabetes mellitus: A two-sample Mendelian randomization study.

BACKGROUND AND AIMS: The role of inflammation in type 2 diabetes mellitus (T2D) remains unclear. We investigated the associations of high sensitivity C-reactive protein (hsCRP) concentration with T2D and glycemic traits using two-sample Mendelian Randomization. METHODS AND RESULTS: We used publically available summary-statistics data from genome-wide association studies on T2D (DIAGRAM: 12 171 cases; 56 862 controls) and glycemic traits (MAGIC: 46 186 participants without diabetes mellitus). We combined the effects of the genetic instrumental variables through inverse-variance weighting (IVW), and MR-Egger regression and weighted-median estimation as sensitivity analyses which take into account potential violations (e.g., directional pleiotropy) of the assumptions of instrumental variable analyses. Analyses were conducted using 15 known hsCRP genetic instruments among which 6 instruments are hsCRP specific and not involved in inflammatory processes beyond hsCRP concentration regulation. Though we found no association between the combined effect of the genetic instrumental variables for hsCRP and T2D with IVW (odds ratio per 1 ln [hsCRP in mg/L]: 1.15; 95% confidence interval: 0.93, 1.42), we found associations for T2D with MR-Egger regression and weighted-median estimation (odds ratio with 95% confidence interval per 1 ln [hsCRP in mg/L], MR-Egger regression: 1.29; 1.08, 1.49; weighted-median estimator: 1.21; 1.02, 1.39). We found no association with T2D for the combination of hsCRP-specific genetic instruments nor did we found associations with glycemic traits in any of the analyses. CONCLUSION: Evidence was provided for a potential causal association between hsCRP and T2D, but only after considering directional pleiotropy. However, hsCRP was not causally associated with glycemic traits.

A critical appraisal of pharmacogenetic inference.

In essence, pharmacogenetic research is aimed at discovering variants of importance to gene-treatment interaction. However, epidemiological studies are rarely set up with this goal in mind. It is therefore of great importance that researchers clearly communicate which assumptions they have had to make, and which inherent limitations apply to the interpretation of their results. This review discusses considerations of, and the underlying assumptions for, utilizing different response phenotypes and study designs popular in pharmacogenetic research to infer gene-treatment interaction effects, with a special focus on those dealing with of clinical effects of drug treatment.

Optimising the ISAR-HP to screen efficiently for functional decline in older patients.

INTRODUCTION: The Identification of Seniors At Risk-Hospitalised Patients (ISAR-HP) has recently been included in guidelines as a frailty indicator to identify patients for comprehensive geriatric assessment. Previous studies showed that the conventional cut-off score incorrectly classifies a high percentage of patients as high risk. We aimed to optimise the predictive value of ISAR-HP by using different cut-offs in older acutely hospitalised patients. METHODS: A prospective follow-up study was performed in two Dutch hospitals. Acutely hospitalised patients aged ≥ 70 years were included. Demographics, illness severity parameters, geriatric measurements and the ISAR-HP scores were obtained at baseline. The primary outcome was a combined end point of functional decline or mortality during 90-day follow-up. RESULTS: In total 765 acutely hospitalised older patients were included, with a median age of 79 years, of whom 276 (36.1%) experienced functional decline or mortality. The conventional ISAR-HP cut-off of ≥ 2 assigned 432/765 patients (56.5%) as high risk, with a positive predictive value (PPV) of 0.49 (95%CI 0.45-0.54) and a negative predictive value of 0.81 (95%CI 0.76-0.85). Thus, 51% of those whom the ISAR-HP denoted as high risk did not experience the outcome of interest. Raising the cut-off to ≥ 4 assigned 205/765 patients (26.8%) as high risk, with a marginally increased PPV to 0.55 (95%CI 0.48-0.62). CONCLUSION: The ISAR-HP with the conventional cut-off of ≥ 2 incorrectly identifies a large group of patients at high risk for functional decline or mortality and raising the cut-off to 4 only marginally improved performance. Caution is warranted to ensure efficient screening and follow-up interventions.

Non-response to (statin) therapy: the importance of distinguishing non-responders from non-adherers in pharmacogenetic studies.

PURPOSE: In pharmacogenetic research, genetic variation in non-responders and high responders is compared with the aim to identify the genetic loci responsible for this variation in response. However, an important question is whether the non-responders are truly biologically non-responsive or actually non-adherent? Therefore, the aim of this study was to describe, within the PROspective Study of Pravastatin in the Elderly at Risk (PROSPER), characteristics of both non-responders and high responders of statin treatment in order to possibly discriminate non-responders from non-adherers. METHODS: Baseline characteristics of non-responders to statin therapy (≤10 % LDL-C reduction) were compared with those of high responders (>40 % LDL-C reduction) through a linear regression analysis. In addition, pharmacogenetic candidate gene analysis was performed to show the effect of excluding non-responders from the analysis. RESULTS: Non-responders to statin therapy were younger (p = 0.001), more often smoked (p < 0.001), had a higher alcohol consumption (p < 0.001), had lower LDL cholesterol levels (p < 0.001), had a lower prevalence of hypertension (p < 0.001), and had lower cognitive function (p = 0.035) compared to subjects who highly responded to pravastatin treatment. Moreover, excluding non-responders from pharmacogenetic studies yielded more robust results, as standard errors decreased. CONCLUSION: Our results suggest that non-responders to statin therapy are more likely to actually be non-adherers, since they have more characteristics that are viewed as indicators of high self-perceived health and low disease awareness, possibly making the subjects less adherent to study medication. We suggest that in pharmacogenetic research, extreme non-responders should be excluded to overcome the problem that non-adherence is investigated instead of non-responsiveness.

Using genetic variation for establishing causality of cardiovascular risk factors: overcoming confounding and reverse causality.

Cardiovascular disease (CVD) remains the leading cause of death in developed countries, despite the decline of CVD mortality over the last two decades. From observational, predictive research, efforts have been made to find causal risk factors for CVD. However, in recent years, some of these findings have been shown to be mistaken. Possible explanations for the discrepant findings are confounding and reverse causation. Genetic epidemiology has tried to address these problems through the use of Mendelian randomisation. In this paper, we discuss the promise and limitations of using genetic variation for establishing causality of cardiovascular risk factors.